A logic framework for addressing medical racism in academic medicine: an analysis of qualitative data
Bibliographic record
Abstract
Abstract Background Despite decades of anti-racism and equity, diversity, and inclusion (EDI) interventions in academic medicine, medical racism continues to harm patients and healthcare providers. We sought to deeply explore experiences and beliefs about medical racism among academic clinicians to understand the drivers of persistent medical racism and to inform intervention design. Methods We interviewed academically-affiliated clinicians with any racial identity from the Departments of Family Medicine, Cardiac Sciences, Emergency Medicine, and Medicine to understand their experiences and perceptions of medical racism. We performed thematic content analysis of semi-structured interview data to understand the barriers and facilitators of ongoing medical racism. Based on participant narratives, we developed a logic framework that demonstrates the necessary steps in the process of addressing racism using if/then logic. This framework was then applied to all narratives and the barriers to addressing medical racism were aligned with each step in the logic framework. Proposed interventions, as suggested by participants or study team members and/or identified in the literature, were matched to these identified barriers to addressing racism. Results Participant narratives of their experiences of medical racism demonstrated multiple barriers to addressing racism, such as a perceived lack of empathy from white colleagues. Few potential facilitators to addressing racism were also identified, including shared language to understand racism. The logic framework suggested that addressing racism requires individuals to understand, recognize, name, and confront medical racism. Conclusions Organizations can use this logic framework to understand their local context and select targeted anti-racism or EDI interventions. Theory-informed approaches to medical racism may be more effective than interventions that do not address local barriers or facilitators for persistent medical racism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".